Power Consumption Model Research Articles

Design of an Optimal Allocator for Power Consumption Minimization in Hexarotor Drone Control Systems

This paper presents an allocator design that considers the power consumption of rotors in the attitude and altitude control system of a hexarotor drone. Based on the power consumption model, the proposed method computes the thrust force that minimizes the total power consumption of the rotor while satisfying the control force constraints required by the controller. To obtain the rotor power consumption model, we conducted experiments on the rotor characteristics using the motors and electronic speed controllers used in the drones. Finally, numerical simulations were performed using the obtained power consumption models to compare the rotor power consumption of the proposed method with that of the conventional method, quantitatively evaluating the effectiveness of the proposed method.

Cell-Free Massive MIMO Power Consumption with Serial Front-Hauls

Massive MIMO deployments have been traditionally based on dedicated links in the front-haul, i.e., between the central processing units and the Access Points (APs). Recently, cell-free massive Multiple-Input Multiple-Output (MIMO) systems based on serial front-haul links have been discussed to simplify the deployments, among other reasons. However, the power consumption models currently used for cell-free massive MIMO deployments typically assume dedicated front-haul links. This paper highlights the inaccuracy of these models when applied to serial front-hauls and proposes simple adaptations to achieve more realistic results. The results obtained for an exemplary scenario show that the front-haul power would represent 61.73% of the total consumed power with the original models. In contrast, with the proposed adaptations, it could be as low as 1.59% of the total consumed power for some serial front-haul configurations. Additionally, the impact of considering APs with lower power consumption is studied, in which case, the percentages above would become 93.15% and 11.96%, respectively. Hence the importance of having power models that fit the front-haul topology.

EV charging load forecasting and optimal scheduling based on travel characteristics

Accurate prediction of EV charging load distribution is not only the basis of evaluating the capacity of distribution network to receive EV, but also the necessary premise to promote the research of charging station planning and vehicle-network interaction. Based on this, firstly, according to the travel characteristics of electric private car and taxi users, this paper constructs the models of speed-flow and EV power consumption per mileage, thus, the real-time changes of vehicle speed and power consumption per mileage are simulated. Secondly, consider factors such as charging price, time, and power consumption along the way, as well as multiple sources of information such as traffic networks, vehicles, public quick charging stations, and distribution networks, the OD matrix analysis method is introduced to simulate EV moving characteristics for EV trip assignment matrix, and the EV charging load forecasting system with real-time interaction of multi-source information is established. Finally, the second-order cone optimization method is used to optimize the space-time distribution of regional charging load. The model and method proposed in this paper are more consistent with the actual situation and can improve the accuracy of prediction.

Comparative Study of LSTM and ANN Models for Power Consumption Prediction of Variable Refrigerant Flow (VRF) Systems in Buildings

The optimized control of variable refrigerant flow (VRF) system requires an accurate time series forecast model for power consumption. Currently, physics-based and black-box models widely used for forecasting power consumption may not be able to capture the dynamic and non-linear behavior of such complex systems. This study presents a long-short-term memory (LSTM), deep learning-based model to accurately predict the power consumption of a VRF system with heat recovery units. The model training used one year of VRF system field test data. The feature selection through the Pearson correlation coefficient was implemented to improve the model's accuracy and computational efficiency. The sensitivity analysis of feature selection was performed by preparing three feature sets, including different levels of relationship with the predicted target. Additionally, the hyperparameters of the models were optimized by Bayesian optimization with the Tree-structured Parzen Estimator algorithm. The deep learning model, LSTM model, was compared to the baseline machine learning model, Artificial Neural Network (ANN) and decision tree. The results show that LSTM-30feat with input time step 4 has the best testing performance of Coefficient of the Variation of the Root Mean Square Error (CvRMSE) 23.3%. The best ANN model is ANN-10feat with input time step 8, which has a CvRMSE of 27.8% in testing and 13,569 trainable parameters. However, LSTM-10feat with input time step 4 has the CvRMSE of 24.8% in testing, and the trainable parameters are 1,809. A higher number of trainable parameters in models might result in increased memory usage on the computer and be computationally expensive.

Beamforming for Multi-Bit Intelligent Reflecting Surface with Phase Shift-Dependent Power Consumption Model.

In recent years, the intelligent reflecting surface (IRS) has attracted increasing attention for its capability to intelligently reconfigure the wireless propagation channel. However, most existing works ignore the dynamic power consumption of IRS related to the phase shift configuration. This relationship gets even more intractable for a multi-bit IRS because of its nonlinearity and implicit form. In this paper, we investigate the beamforming optimization for multi-bit IRS-aided systems with the practical phase shift-dependent power consumption (PS-DPC) model, aiming at minimizing the power consumption of the system. To solve the implicit and nonlinear relationship, we introduce a selection matrix to explicitly represent the power consumption and the phase shift matrix of the IRS, respectively. Then, we propose a generalized Benders decomposition-based beamforming optimization algorithm in the single-user scenario. Furthermore, in the multi-user scenario, we design a coordinate descent-based algorithm and a genetic algorithm for the beamforming optimization. The simulation results show that the proposed algorithms significantly decrease the power consumption of the multi-bit IRS-aided systems.

Electric Vehicle Power Consumption Modelling Method Based on Improved Ant Colony Optimization-Support Vector Regression

Electric Vehicle Power Consumption Modelling Method Based on Improved Ant Colony Optimization-Support Vector Regression

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

Power overwhelming: the one with the oscilloscopes

Visualization as a discipline has to investigate its practical implications in a world steadily moving toward greener computing methods. Quantifying the power consumption of visualization algorithms is thus essential, given the ever-increasing energy needs of GPUs. Previous approaches rely on integrated sensors or invasive methods that require modifications and special test setups. However, they still suffer from imprecision from low sampling rates and integration over time. Using a high-precision, high-frequency setup via steerable oscilloscopes, we can objectively measure the resulting quality of previous approaches. This is essential to establish a ground truth, pave the way for improved modeling of power consumption in general, and enable better estimates based on the output of lower-quality sensors. We finally discuss benefits that can be drawn from the additional insight of the higher-precision setup and which additional use cases can justify the incurred costs.Graphical abstract

Forecasting operation of a chiller plant facility using data-driven models

In recent years, data-driven models have enabled accurate prediction of chiller power consumption and chiller coefficient of performance (COP). This study evaluates the usage of time series Extreme Gradient Boosting (XGBoost) models to predict chiller power consumption and chiller COP of a water-cooled chiller plant. The 10-second measured data used in this study are from the Intelligent Building Agents Laboratory (IBAL), which includes two water-cooled chillers. Preprocessing, data selection, noise analysis, and data smoothing methods influence the accuracy of these data-driven predictions. The data intervals were changed to 30 s, 60 s, and 180 s using down-sampling and averaging strategies to investigate the impact of data preprocessing methods and data resolutions on the accuracy of chiller COP and power consumption models. To overcome the effect of noise on the accuracy of the models of chiller power consumption and COP, two data smoothing methods, the moving average window strategy and the Savitzky-Golay (SG) filter, are applied. The results show that both methods improve the predictions compared to the baseline, with the SG filter slightly outperforming the moving average. Particularly, the mean absolute percentage error of the chiller COP and power consumption models improved from 4.8 to 4.9 for the baseline to 1.9 and 2.3 with the SG filter, respectively. Overall, this study provides a practical guide to developing XGBoost data-driven chiller power consumption and COP prediction models.

Fault Detection and Diagnosis of Three-Wheeled Omnidirectional Mobile Robot Based on Power Consumption Modeling

Three-wheeled omnidirectional mobile robots (TOMRs) are widely used to accomplish precise transportation tasks in narrow environments owing to their stability, flexible operation, and heavy loads. However, these robots are susceptible to slippage. For wheeled robots, almost all faults and slippage will directly affect the power consumption. Thus, using the energy consumption model data and encoder data in the healthy condition as a reference to diagnose robot slippage and other system faults is the main issue considered in this paper. We constructed an energy model for the TOMR and analyzed the factors that affect the power consumption in detail, such as the position of the gravity center. The study primarily focuses on the characteristic relationship between power consumption and speed when the robot experiences slippage or common faults, including control system faults. Finally, we present the use of a table-based artificial neural network (ANN) to indicate the type of fault by comparing the modeled data with the measured data. The experiments proved that the method is accurate and effective for diagnosing faults in TOMRs.

Materials and Structural Designs toward Motion Artifact-Free Bioelectronics.

Bioelectronics encompassing electronic components and circuits for accessing human information play a vital role in real-time and continuous monitoring of biophysiological signals of electrophysiology, mechanical physiology, and electrochemical physiology. However, mechanical noise, particularly motion artifacts, poses a significant challenge in accurately detecting and analyzing target signals. While software-based "postprocessing" methods and signal filtering techniques have been widely employed, challenges such as signal distortion, major requirement of accurate models for classification, power consumption, and data delay inevitably persist. This review presents an overview of noise reduction strategies in bioelectronics, focusing on reducing motion artifacts and improving the signal-to-noise ratio through hardware-based approaches such as "preprocessing". One of the main stress-avoiding strategies is reducing elastic mechanical energies applied to bioelectronics to prevent stress-induced motion artifacts. Various approaches including strain-compliance, strain-resistance, and stress-damping techniques using unique materials and structures have been explored. Future research should optimize materials and structure designs, establish stable processes and measurement methods, and develop techniques for selectively separating and processing overlapping noises. Ultimately, these advancements will contribute to the development of more reliable and effective bioelectronics for healthcare monitoring and diagnostics.

Predicting the dynamic behavior of a magnetocaloric cooling prototype via artificial neural networks

Although magnetocaloric cooling is considered a promising long-term alternative to vapor compression, recent prototype developments have not yet made this technology commercially competitive, primarily due to its high energy consumption and lack of cost-effective, long-term mechanically-chemically stable materials. To address the first issue and understand how the efficiency of magnetocaloric systems can be improved, dynamic models can offer valuable insights into their transient operation. This work focuses on the development of an artificial neural network with experimental data to model the dynamic operation of a magnetic refrigeration system. Through a design of experiments approach, we propose excitation signals for the identification experiment, involving five manipulated variables and one selected disturbance as inputs, with the output temperature of the cold manifold and power consumption as the target parameters. We chose a nonlinear autoregressive artificial neural network with exogenous inputs to model the transient operation of the system. The temperature model achieved R2 values of 0.995 and 0.955 for the 1-step and 90-step ahead predictions, respectively. Similarly, the power consumption model achieved R2 values of 0.988 and 0.949 for the 1-step and 90-step ahead predictions, respectively. These performance metrics were evaluated on the test sets that were not used for training the models, highlighting the robustness and accuracy of the models in both short-term and long-term predictions.

An Adaptive Actuator Driver for AC Contactors

The actuator drivers are rarely transplanted among different contactors because the self-tuning of excitation control parameters either takes a long time or is relevant to many contactor structural parameters. An adaptive actuator driver for AC contactors is proposed based on the coil impedance estimation and mathematical equations between the coil impedance and excitation control parameters. The coil impedance estimation and its error correction are established through the voltage balance equation when the contactor is in the open position. The coil inductance estimation is established through the zero-state response equation when the contactor is in the closed position. The mathematical equations are established based on the equivalence of the electromagnetic force under different excitation methods, the power consumption model, and the control system's transfer function. A geometric feature of the flux linkage waveform is established to identify whether a contactor is in the closed position and realize the self-transition of the driver's working state. Then, self-tuning regulators for the excitation control parameters are realized. This driver has a plug-and-play function for AC contactors and can reduce the contactor's power consumption. This driver may be prospectively reduced to a chip.

UGV에서 효율적인 노면 모니터링을 위한 퓨전 센서 시스템

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of =10, =10.

Narrowband Internet of Things via Low Earth Orbit Satellite Networks: An Efficient Coverage Enhancement Mechanism Based on Stochastic Geometry Approach.

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks.

Modelling and optimisation of power consumption and productivity in milling of thin-walled parts

A high percentage of mechanical parts in the metalworking industry (automotive, medical, energetic sector, and etc.), refers to thin-walled structures with optimized shapes and dimension, which produce on high or semi batch production principle. Production processes management, in mentioned industry and production systems, requires the development of appropriate information technologies based automatic systems. Planning and control systems are based on computer-aided technologies. Basis of this system are mathematical-logical models commonly, which is quantitatively link between input process parameters and process performance indicators (output parameters). In this study, modelling of total power consumption and process productivity was performed. Total power consumption and the process productivity were modelled, as very important output indicators in efficient and sustainable production. The modelling is based on the experimental analysis of the machining process during machining of thin-walled parts made of C45E (AISI 1045) carbon steel. Modelling was performed using the least squares method, with the use of ANOVA statistical analysis for the purpose of defining the input factors significance. Depth of cut, milling width and feed per tooth were used as input cutting parameters. Input cutting parameters combined experimental analysed according to Taguchi experimental plan. Based on developed adequate and sufficiently accurate mathematical models, optimization procedure was performed. The goal of optimisation was to minimize the power consumption and maximise productivity.

Cognitive Modelling and Forecasting of Electricity Consumption

Purpose of reseach. Development of a forecast model of energy consumption and assessment of factors influencing its consumption. The obtained forecast estimates of energy consumption will improve the quality and efficiency of management decisions at all levels of administrative management.Methods. The article presents an analytical review of the existing methods of cognitive modelling and forecasting of electric power consumption, the description of the software implementation of the information-computing system that allows to make a forecast of electric power consumption by the population of the administrative-territorial formation. The approach to the description of factors of electric power consumption by both population and various branches of national economy, as well as organisations engaged in rendering various services has been proposed. Special software has been developed, which allows to obtain model results of electric power consumption in an automated mode, to carry out factor analysis of power consumption. The experimental verification of the work of the programme of cognitive modelling and forecasting of electric power consumption by the population of Lgovsky district of Kursk region is given. The developed software also makes it possible to evaluate the adequacy of the obtained results and promptly adjust the model parameters.Results. As a result of the research a fuzzy cognitive map of energy consumption for a municipal entity was developed. The concepts of the subject area describing the influence of various groups of factors on the level of electric energy consumption were identified. Forecast estimates of electricity consumption were obtained, which were based on the data for the retrospective period. Adequacy indicators based on the calculation of statistical criteria are determined for the obtained estimates.Conclusion. The results of the study have shown that the combination of cognitive and statistical methods allows to achieve an adequate solution when solving the problem of energy consumption forecasting.

Analysis of integrated networks: super-EPON-WiMAX and super-EPON-LTE, and power consumption modeling

Analysis of integrated networks: super-EPON-WiMAX and super-EPON-LTE, and power consumption modeling

Optimization of the Structural and Motion Parameters of Blade Cutters in Paddy Field Pulping Machines

Blade cutters are a component in paddy field pulping machines that perform mud splashing, and the design of their structural and motion parameters will directly affect the splashed-mud volume and pulping-machine efficiency. Therefore, the optimization of the blade cutter’s structural and motion parameters is an important approach for improving the operating performance of paddy field pulping machines. In this study, based on the central-composite-design (CCD) method and a response-surface-method-based variance analysis, a regression-forecast model for the relationship between the splashing performance of the blade cutter and the blade’s structural and motion parameters was constructed to determine the influence of these parameters on the multi-dimensional splashing performance of blade cutters. Additionally, with the construction of a multi-objective performance-optimization model for pulping-machine blade cutters, the predicted optimal structural and motion parameters could be obtained based on the genetic algorithm. The ideal operating performance could be achieved when the blade turning radius was 180 mm, with a bending angle of 125°, a sub-cutter dip angle of 63°, a forward velocity of 0.15 m/s, and a rotating speed of 158 r/min. Verification of the optimization results in a bench test showed that the mean relative errors between the theoretical and experimental values of the mud volume and power consumption were 9.13% and 8.86%, respectively, revealing the high accuracy of the mud-volume and power-consumption models. Furthermore, there was a significant reduction in blade-cutter unit power consumption of 19.13%. These research results can provide a theoretical reference and technical support for blade-cutter optimization and improving pulping-machine performance.

Methods of power consumption in conditions of high-productive areas of coal mines

The publication analyses the existing methods of estimated power consumption in relation to the conditions of high-productivity coal mine sites. In addition, a mathematical description of the model of predictive power consumption using the method of correlation and regression analysis is proposed and an algorithm for determining the parameters of specific power consumption in relation to the conditions of high-productivity coal mine sites is proposed. Key words: coal mine; high-performance section, technological equipment, specific power consumption; algorithm; mathematical forecast model of power consumption